1. Technological Field
This disclosure pertains generally to medical imaging, and more particularly to automatically segmenting of 3D medical images and reconstructing structural surfaces from the medical images.
2. Background Discussion
One important field for medical imaging is in diagnosing brain conditions. Segmentation and reconstruction of cerebral cortical surfaces from T1-weighted magnetic resonance images (MRI) is an important issue in neuroscience and medicine. This procedure is required for quantitative brain analysis, visualization, cortical mapping and surgical planning.
Many approaches have been presented for brain extraction that utilize deformable models which are initialized either manually, or automatically, but that operate for half-head data only (i.e., the less complex upper half). Typically, filling of ventricles is a manual step, or it is based on a registration process, such as atlas registration which is a slow processor intensive technique which generally requires more than about ten hours. During surface reconstruction, many approaches merely address the problem of reconstructing the white matter (WM) surface or the geometric central layer of the cerebral cortex. Some reconstructing techniques utilize a deformation model initialized with a sphere to achieve a final surface with spherical topology, which requires significant processing that generally requires more than twenty hours.
In addition, at the present time the process of performing segmentation and reconstruction involves significant levels of human intervention/interaction to provide accurate representations.
Accordingly, a need exists for fully automated processing of segmentation and reconstruction on MRI images of cerebral cortical surfaces.